Generative oversampling method (GenOMe) for imbalanced data on apnea detection using ECG data

H. R. Sanabila, Ilham Kusuma, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

One of machine learning problem that is difficult but important to be addressed is imbalanced data where particular data is recessive while the others are dominant. Most of classifiers performance significantly degraded when dealing with imbalanced data. The major approaches to tackle imbalanced data are cost sensitive learning which modifies the classifier and resampling which modifies the data distribution. In this research, we employed generated oversampling method (GenOMe) that generate new data point with a particular distribution as a constraint. We examine three distribution functions: Beta, Gamma, and Gaussian distribution. We use Logistic Regression, Support Vector Machine (SVM), and Naive Bayes as classifier to assure the robustness of GenOMe. The experimental results shows that GenOMe outperforms classification using original data and classification using SMOTe (Synthetic Minority Oversampling Technique) data.

Original languageEnglish
Title of host publication2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages572-579
Number of pages8
ISBN (Electronic)9781509046294
DOIs
Publication statusPublished - 6 Mar 2017
Event8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 - Malang, Indonesia
Duration: 15 Oct 201616 Oct 2016

Publication series

Name2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Conference

Conference8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
Country/TerritoryIndonesia
CityMalang
Period15/10/1616/10/16

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